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    Image classification

    Description

    This BE is about image classification. The goal is to classify images from CIFAR10 dataset and evaluate the accuracy of the models, firstly by using a simple KNN and then by using a manually implemented MLP with one hidden layer. The MLP is implemented using numpy and the forward and backward passes are implemented using the chain rule. The MLP is trained using SGD and the gradients are computed using backpropagation. The MLP is trained using the CIFAR10 dataset and the accuracy is evaluated on the test set.

    Documentation

    The be_image_classification.ipynb file contains the main code used to train and evaluate the models.

    The functions implemented and used in this BE are located in the utils folder.

    The images located in the images folder are:

    • knn_accuracy.png: the evolution of the accuracy of the model with respect to the number of neighbors
    • mlp_loss.png: the evolution of the loss of the model with respect to the number of epochs, for the manually implemented MLP
    • mlp_loss_tf.png: the evolution of the accuracy of the equivalent model implemented using tensorflow with respect to the number of epochs to compare with the manually implemented MLP and check that the implementation is correct